Visual SmileyNet

Visual SmileyNet is a library for training an image to emoji neural network model. It contains functionality used to produce the results in the paper:

Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis Z. Al-Halah, A. Aitken, W. Shi, J. Caballero, ICCV Workshops, 2019

For more information on this work please visit the project page.

If you use this code please reference the publication above.

Installation

All requirements can be install by running: python setup.py install

Requirements

With python=3.7:

torch==1.2.0
torchvision==0.4.0
numpy==1.16.6
pillow==6.2.2
pandas==0.24.0
requests==2.22.0

Visual Smiley Dataset

You can download the visual smiley dataset used in this work from here.

Getting Started

Training

The script train_model.sh will train a network to perform image to emoji predictions. The script train_model_toy.sh will run the training of a model on a very small subset of the dataset. This can be used to get familiar with the training script before running on the full dataset.

To start training a model on the Visual SmileyNet dataset you can follow these steps:

1. Download the Visual SmileyNet dataset from here.
2. Extract the dataset and save the folder location in environment variable DATA_DIR with export DATA_DIR=</dataset/location/Visual Smiley Dataset>
3. Run the training script with sh sh_scripts/train_model.sh. Alternatively, running sh sh_scripts/train_model_toy.sh will run on small subset of the dataset.

Note that images are fetched online during training, which may slow down training runtime.

Testing

The script test_model.sh will test the model on a set of images.

Usage: sh sh_scripts/test_model.sh <path-to-model-file>

Predicting emojis

The script predict_emojis.sh will find the top emoji predictions for a folder of images.

Usage: sh sh_scripts/predict_emojis.sh <path-to-model-file> <path-to-dir-of-images>

Cite us

@inproceedings{visualsent_iccv_cromol2019,
    title={Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis},
    author={Ziad Al-Halah and Andrew Aitken and Wenzhe Shi and Jose Caballero},
    booktitle={IEEE International Conference on Computer Vision (ICCV) Workshops},
    arxivId = {1907.06160},
    year={2019}
}

Security Issues?

Please report sensitive security issues via Twitter's bug-bounty program (https://hackerone.com/twitter) rather than GitHub.